Benefits of Building a Data Product

It turns out that instead of rapidly unlocking insights for the business, your data engineers are most likely bogged down in technical details, concerned about software code, QA, reliability, cloud security, and compute costs. They’re worried about IT problems — not business problems, and certainly not usability. Stakeholders have grown frustrated with how long it takes to build data pipelines. When they are finally ready, it’s unclear where the outputs are coming from and what they’re composed of. Business users are questioning the accuracy and data reliability of the data pipelines and often have shifted back to operating on hunches rather than facts.

Looking to invigorate users with data they can quickly turn into business value? Learn 5 key benefits of the data product approach.

Centralised Registry

Data products should be easy to navigate and explore by keeping centralised data catalog or registry that publishes their ownership, metadata, operations agreement and other policies. The catalog or registry serves as the “one-stop-shop” entry point for those who are keen on exploring and consuming datasets from a data product. In addition, an enterprise can enable the structural engagement to start with a data product.

Each Pokemon card is the registry of a monster’s height, weight, health point, special ability and more

Reusable Data Asset (Differentiation)

Unlike use case specific datasets, a data product delivers a high-quality, ready-to-use set of data that people across an organisation can easily access and apply to different business challenges. For example, a data product could provide a 360-degree view of an important entity, such as customers, employees, product lines, or branches. Or it could deliver a given data capability, such as a digital twin that replicates the operation of real-world assets. This benefit eliminates duplication of effort to build similar sets of data over and over, and introduce the cross-functional resources versus the siloed.

Books in library are sorted in a way that people can easily navigate and find them

Ready for Consumption

Data products are wired to enable standard types of consumption. Data products incorporate the wiring necessary for different business systems, such as digital apps or reporting systems, to “consume” the data. Each type of business system has its own set of requirements for how data is stored, processed, and managed; we call these “consumption archetypes.” Once consumption archetypes are standardised and the wiring patterns are established, the enterprise reuses blueprints (design) and modular technologies required to connect from end to end, across products.

Real World Contextualisation

By definition, “contextualisation” means adding related information to something to make it more useful. This can mean including background information, patterns, trends, outliers, and more to help a data consumer make sense of what the data is really saying.

Master data, as a data product, represents “data about the business entities that provide context for business transactions”. The most commonly found categories of master data are employees, products, applications and geographical information.

Maintaining master data as a data product gives a business an opportunity to enrich their transactional data with context. This eventually promotes a business to better interpret data, while mapping each record to business entities, exploring distribution, and enabling segmentation based on entity. As a result, a business can slice and dice data in various ways.

Put simply. Macro lens shoot at low levels of magnification which can be compared with micro lens.

Quality Assurance & Continuous Improvement

In a data product architecture, each data product is maintained and owned by a dedicated data domain team.

As a subject matter expert (SME) for each product, the team is of paramount importance to manage requirements / coverage / changes that have impact on integrity of data for continuous improvement.

As a dedicated custodian with data ownership, the team follows the compliance of federated governance framework from the centralised platform team. They ensure that their product is available for data consumers with up-to-date metadata and contract, and reliable with no outstanding data quality incidents while assuring quality. When a data incident occurs, the team triages and remediates it under a sever-level agreement (SLA) that states RTO (Response Time Objective) and MAO (Maximum Allowable Outage).